Cervical disease (CC) is one of the most well-known malignancies on the planet which cause riskier to the human existence and can be very difficult to cure once the cancer sets in our body. To work on the location and expectation of the sickness, this interaction was acquainted with work on the analytic exactness of Cervical Cancer in such dataset pictures. Thus, we attempted to propose a wise and effective grouping model for Cervical Cancer in view of convolutional brain organization (Convolutional Neural Network) with somewhat basic design contrasted and others. Likewise, we proposed a simple and functional technique for the grouping of Cervical Cancer from cytological pictures with effective component extraction or exact cell picture division work. This system aims to predict cervical cancer with good accuracy and precision and it also shows the stage the cancer is on. Once the stage has been detected, the system then goes on to automatically generate the diagnosis step that the patient can take. Thus, helping patients take necessary steps in order to cure themselves of this deadly disease before it become life threatening. The programmed cell identification results are contrasted and the physically commented on ground truth and other cutting edge cell location calculations.
₹10000 (INR)
NON IEEE -2022